5.3 Spatial intersections

5.3.1 Clip operation

bahn_haltestelle_ch <- st_read("sample_data/other/Haltestelle_Bahn_CH.shp")

## Reading layer `Haltestelle_Bahn_CH' from data source `C:\Users\bako\Desktop\Projekte\All_thing_R_Spatial\github\spatialR_for_ArcGIS_Users\sample_data\other\Haltestelle_Bahn_CH.shp' using driver `ESRI Shapefile'
## Simple feature collection with 3134 features and 16 fields
## geometry type:  POINT
## dimension:      XYZ
## bbox:           xmin: 2488908 ymin: 1076850 xmax: 2817389 ymax: 1289090
## z_range:        zmin: 194.905 zmax: 3453.525
## projected CRS:  CH1903+ / LV95

kantonsgebiet <- st_read("sample_data/other/Kantonsgebiet.shp")

## Reading layer `Kantonsgebiet' from data source `C:\Users\bako\Desktop\Projekte\All_thing_R_Spatial\github\spatialR_for_ArcGIS_Users\sample_data\other\Kantonsgebiet.shp' using driver `ESRI Shapefile'
## Simple feature collection with 51 features and 22 fields
## geometry type:  POLYGON
## dimension:      XYZ
## bbox:           xmin: 2485410 ymin: 1075268 xmax: 2833858 ymax: 1295934
## z_range:        zmin: 193.51 zmax: 4613.729
## projected CRS:  CH1903+ / LV95

tm_shape(kantonsgebiet) + tm_polygons("#f0f0f0") + 
  tm_shape(bahn_haltestelle_ch) + tm_dots(col = "#e34a33",size = 0.1, alpha = 0.5) + 
  tm_shape(kantonsgebiet) + tm_borders(col = "black") + tm_layout(frame = F)

We want now to analyse the situation at a particular area. So we are going to create an index with the specific cantons we are interested in.

index <- kantonsgebiet$name == "Zürich" |
  kantonsgebiet$name == "St. Gallen" |
  kantonsgebiet$name == "Thurgau" |
  kantonsgebiet$name == "Aargau"

kantons_aOi <- kantonsgebiet[index,]

hal_clipped <- st_intersection(kantons_aOi,bahn_haltestelle_ch)

Plot the result from the clipping operation

tm_shape(kantons_aOi) + tm_polygons("#f0f0f0") + 
  tm_shape(hal_clipped) + tm_dots(col = "#e34a33",size = 0.1, alpha = 0.5) + 
  tm_shape(kantons_aOi) + tm_borders() + tm_layout(frame = F)

So, ultimately, as we can see above, the st_intersection function creates a datasets where the point dataset is precisely “clipped” based on the area of interest.

library(sf)
library(tidyverse)
library(raster)